INTRODUCTION TO DSM

INPUT REQUIREMENTS

Day 1: Part 2

INPUT 1: DATA

Input data requirements

◦ Existing soil maps

◦ Soil profile data

◦ Lab analytical and field observation soil data

◦ Climate data

◦ Other maps –Altitude, Geology, Land use/cover

Typical sources of input DSM data

Input data Source Level of detail (Resolution)

< 20 m 20 – 200 m > 200 m

Land use/ land cover Multi spectral remote

sensing images

GeoEye, Quickbird,

Ikonos, SPOT

Landsat,

ASTER,

MODIS, AVHRR,

MERIS

Hyper-spectral remote

sensing images

AVIRIS

Radar, radiometry LIDAR ASAR, MWR

Vegetation/land cover GLOBCOVER

Relief DEM National Contour

or Topomaps

ASTER, SRTM GTOPO

Climate Climate (rainfall) data National archives MARS, AVHRR

Parent material Geology maps National archives

Geological surveys Regional studies Gamma –ray

spectrometry

Global geology

map

Soil Soil profile/properties Regional soil

surveys

National, ISRIC, FAO

INPUT 2: DSM Methods Spatial interpolation

◦ To make smooth trend over discrete locations

Digital terrain models

◦ To derive relief characteristics

Remote sensing analysis

◦ To extract land use and land cover characteristics

Statistical modelling

◦ To explore and understand data characteristics

◦ To model relationships

◦ To quantify confidence in inputs and outputs

DSM Tools and Software

Method Tools Software

Spatial interpolationGeostatistics R

Non-geostatistical method QGIS, ILWIS

Terrain analysis Digital Terrain modelling SAGA, QGIS

Remote sensing analysisImage correction ILWIS, QGIS

Image Indices ILWIS

Statistical analysisMultivariate analysis ILWIS, R

Correlation analysis R

Database managementStorage MS Office

Dissemination Google Earth

Legacy data

All existing soil information collected to characterize or

map soils

◦ landscape and site descriptions,

◦ soil profile morphological descriptions

◦ laboratory analysis of the main chemical, physical and biological

soil properties

◦ Soil maps

◦ Geophysical/geotechnical surveys

Other maps – climate, geology, land use, contour and

topographic maps

Tacit knowledge - reports, legends, mental models

Importance of legacy data

Model calibration/validation

Potential in reducing cost of new samples

Core of predictors (soil forming factors)

Enrich interpretation of spatial models

As baseline data for monitoring

Input into SCORPAN modelling

Problems with legacy data

Documentation is usually with gaps

Original authors may not be available

Harmonization issues

◦ Quality (error), language,

◦ Georeferencing (lack/un-clear/diff. projection)

◦ Map units (proportions, classes, impurities)

◦ Classification (names, taxonomy, ref. properties)

Uniformity issues (sampling, depth, units, etc)

Examples of legacy data

Scanned soil map

http://eusoils.jrc.ec.europa.eu/esdb_archive/

eudasm/africa/lists/k10_cke.htm

Soil samples from a government agency

Soil profiles from ISRIC

Scanned soil mapLegend